Temperature-dependent cell death patterns induced by functionalized gold nanoparticle photothermal therapy in melanoma cells
Bibliographic record
Abstract
Photothermal therapy (PTT) is a promising approach for cancer targeting therapy. However, the temperature-dependent killing of tumor cells in PTT remains unclear. In this study, we report necroptosis plays a role in the anti-tumor effects observed in gold nanorod (GNR)-mediated PTT in melanoma. We first synthesized gold nanorods with a targeting adaptor FA (GNRs-FA), which achieved high efficacy of targeted delivery to melanoma cells. We further demonstrated PTT, precipitated by GNRs-FA under the induction of near-infrared laser, was temperature-dependent. Furthermore, the photothermal killing of melanoma cells showed different patterns of cell death depending on varying temperature in PTT. In a lower temperature at 43 °C, the percentages of apoptosis, necroptosis and necrosis of tumor cells were 10.2%, 18.3%, and 17.6%, respectively, suggesting the cell killing is ineffective at lower temperatures. When the temperature increased to 49 °C, the cell death pattern switched to necrosis dominant (52.8%). Interestingly, when the PTT achieved a moderate temperature of 46 °C, necroptosis was significantly increased (35.1%). Additionally, GNRs-FA/PPT-mediated necroptosis was regulated by RIPK1 pathway. Taken together, this study is the first to demonstrate that temperature-dependent necroptosis is an important mechanism of inducing melanoma cell death in GNR-mediated PTT in addition to apoptosis and necrosis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".